condtour: Conditional tour; a tour through sections in data space
In markajoc/condvis: Conditional Visualization for Statistical Models

Description Usage Arguments Value See Also Examples

Whereas ceplot allows the user to interactively choose sections to visualise, condtour allows the user to pre-select all sections to visualise, order them, and cycle through them one by one. ']' key advances the tour, and '[' key goes back. Can adjust threshold for the current section visualisation with ',' and '.' keys.

condtour(data, model, path, response = NULL, sectionvars = NULL,
  conditionvars = NULL, threshold = NULL, lambda = NULL,
  distance = c("euclidean", "maxnorm"), view3d = FALSE,
  Corder = "default", conf = FALSE, col = "black", pch = NULL,
  xsplotpar = NULL, modelpar = NULL, xcplotpar = NULL)

`data`	A dataframe.
`model`	A fitted model object, or a list of such objects.
`path`	A dataframe, describing the sections to take. Basically a dataframe with its `colnames` being `conditionvars`.
`response`	Character name of response variable in `data`.
`sectionvars`	Character name(s) of variables in `data` on which to take sections.
`conditionvars`	Character name(s) of variables in `data` on which to condition.
`threshold`	Threshold distance. Observed data which are a distance greater than `threshold` from the current section are not visible. Passed to `similarityweight`.
`lambda`	A constant to multiply by number of factor mismatches in constructing a general dissimilarity measure. If left `NULL`, behaves as though `lambda` is set greater than `threshold`, and so only observations whose factor levels match the current section are visible. Passed to `similarityweight`.
`distance`	The type of distance measure to use, either `"euclidean"` (default) or `"maxnorm"`.
`view3d`	Logical; if `TRUE`, plots a three-dimensional regression surface when possible.
`Corder`	Character name for method of ordering conditioning variables. See `arrangeC`.
`conf`	Logical; if `TRUE`, plots confidence bounds or equivalent when possible.
`col`	Colour for observed data points.
`pch`	Plot symbols for observed data points.
`xsplotpar`	Plotting parameters for section visualisation as a list, passed to `plotxs`. Not used.
`modelpar`	Plotting parameters for models as a list, passed to `plotxs`. Not used.
`xcplotpar`	Plotting parameters for condition selector plots as a list, passed to `plotxc`. Can specify `cex.axis`, `cex.lab` , `tck`, `col` for highlighting current section, `cex`.

Produces a set of interactive plots. One device displays the current section. A second device shows the the current section in the space of the conditioning predictors given by conditionvars. A third device shows some simple diagnostic plots; one to show approximately how much data are visible on each section, and another to show what proportion of data are visited by the tour.

ceplot, similarityweight

## Not run: 

data(powerplant)
library(e1071)
model <- svm(PE ~ ., data = powerplant)
path <- makepath(powerplant[-5], 25)
condtour(data = powerplant, model = model, path = path$path,
  sectionvars = "AT")

data(wine)
wine$Class <- as.factor(wine$Class)
library(e1071)
model5 <- list(svm(Class ~ ., data = wine))
conditionvars1 <- setdiff(colnames(wine), c("Class", "Hue", "Flavanoids"))
path <- makepath(wine[, conditionvars1], 50)
condtour(data = wine, model = model5, path = path$path, sectionvars = c("Hue"
  , "Flavanoids"), threshold = 3)


## End(Not run)